Physics and Imaging in Radiation Oncology最新文献

Background and purpose

In online adaptive magnetic resonance image (MRI)-guided radiotherapy (MRIgRT), manual contouring of rectal tumors on daily images is labor-intensive and time-consuming. Automation of this task is complex due to substantial variation in tumor shape and location between patients. The aim of this work was to investigate different approaches of propagating patient-specific prior information to the online adaptive treatment fractions to improve deep-learning based auto-segmentation of rectal tumors.

Materials and methods

243 T2-weighted MRI scans of 49 rectal cancer patients treated on the 1.5T MR-Linear accelerator (MR-Linac) were utilized to train models to segment rectal tumors. As benchmark, an MRI_only auto-segmentation model was trained. Three approaches of including a patient-specific prior were studied: 1. include the segmentations of fraction 1 as extra input channel for the auto-segmentation of subsequent fractions, 2. fine-tuning of the MRI_only model to fraction 1 (PSF_1) and 3. fine-tuning of the MRI_only model on all earlier fractions (PSF_cumulative). Auto-segmentations were compared to the manual segmentation using geometric similarity metrics. Clinical impact was assessed by evaluating post-treatment target coverage.

Results

All patient-specific methods outperformed the MRI_only segmentation approach. Median 95th percentile Hausdorff (95HD) were 22.0 (range: 6.1–76.6) mm for MRI_only segmentation, 9.9 (range: 2.5–38.2) mm for MRI+prior segmentation, 6.4 (range: 2.4–17.8) mm for PSF_1 and 4.8 (range: 1.7–26.9) mm for PSF_cumulative. PSF_cumulative was found to be superior to PSF_1 from fraction 4 onward (p = 0.014).

Conclusion

Patient-specific fine-tuning of automatically segmented rectal tumors, using images and segmentations from all previous fractions, yields superior quality compared to other auto-segmentation approaches.

Background and purpose

No best practices currently exist for achieving high quality radiation therapy (RT) treatment plan adaptation during magnetic resonance (MR) guided RT of prostate cancer. This study validates the use of machine learning (ML) automated RT treatment plan adaptation and benchmarks it against current clinical RT plan adaptation methods.

Materials and methods

We trained an atlas-based ML automated treatment planning model using reference MR RT treatment plans (42.7 Gy in 7 fractions) from 46 patients with prostate cancer previously treated at our institution. For a held-out test set of 38 patients, retrospectively generated ML RT plans were compared to clinical human-generated adaptive RT plans for all 266 fractions. Differences in dose-volume metrics and clinical objective pass rates were evaluated using Wilcoxon tests (p < 0.05) and Exact McNemar tests (p < 0.05), respectively.

Results

Compared to clinical RT plans, ML RT plans significantly increased sparing and objective pass rates of the rectum, bladder, and left femur. The mean ± standard deviation of rectum D20 and D50 in ML RT plans were 2.5 ± 2.2 Gy and 1.6 ± 1.3 Gy lower than clinical RT plans, respectively, with 14 % higher pass rates; bladder D40 was 4.6 ± 2.9 Gy lower with a 20 % higher pass rate; and the left femur D5 was 0.8 ± 1.8 Gy lower with a 7 % higher pass rate.

Conclusions

ML automated RT treatment plan adaptation increases robustness to interfractional anatomical changes compared to current clinical adaptive RT practices by increasing compliance to treatment objectives.

Multiple tools are available for commissioning and quality assurance of deformable image registration (DIR), each with their own advantages and disadvantages in the context of radiotherapy. The selection of appropriate tools should depend on the DIR application with its corresponding available input, desired output, and time requirement. Discussions were hosted by the ESTRO Physics Workshop 2021 on Commissioning and Quality Assurance for DIR in Radiotherapy. A consensus was reached on what requirements are needed for commissioning and quality assurance for different applications, and what combination of tools is associated with this.

For commissioning, we recommend the target registration error of manually annotated anatomical landmarks or the distance-to-agreement of manually delineated contours to evaluate alignment. These should be supplemented by the distance to discordance and/or biomechanical criteria to evaluate consistency and plausibility. Digital phantoms can be useful to evaluate DIR for dose accumulation but are currently only available for a limited range of anatomies, image modalities and types of deformations.

For quality assurance of DIR for contour propagation, we recommend at least a visual inspection of the registered image and contour. For quality assurance of DIR for warping quantitative information such as dose, Hounsfield units or positron emission tomography-data, we recommend visual inspection of the registered image together with image similarity to evaluate alignment, supplemented by an inspection of the Jacobian determinant or bending energy to evaluate plausibility, and by the dose (gradient) to evaluate relevance. We acknowledge that some of these metrics are still missing in currently available commercial solutions.

Background and purpose

Cardiac implanted electronic devices (CIED) require dose monitoring during each fraction of radiotherapy, which can be time consuming and may have delayed read-out times. This study explores the potential of Cherenkov imaging combined with scintillation dosimetry as an alternative verification system.

Methods and materials

Time-gated, complementary metal–oxide–semiconductor (iCMOS) cameras were used to collect video images of anthropomorphic phantoms and patients undergoing radiation treatment near chest wall cardiac devices. Scintillator discs and optically stimulated luminescence dosimeters (OSLDs) were used for dose measurement. Accuracy of spatial delivery was assessed by overlaying predicted surface dose outlines derived from the treatment planning system (TPS) with the Cherenkov images. Dose measurements from OSLDs and scintillators were compared.

Results

In phantom studies, Cherenkov images visibly indicated when dose was delivered to the CIED as compared to non-overlapping dose deliveries. Comparison with dose overlays revealed congruence at the planned position and non-congruence when the phantom was shifted from the initial position. Absolute doses derived from scintillator discs aligned well with the OSLD measurements and TPS predictions for three different positions, measuring within 10 % for in-field positions and within 5 % for out-of-field positions. For two patients with CIEDs imaged over 18 fractions, Cherenkov imaging confirmed positional accuracy for all fractions, and dose measured by scintillator discs deviated by <0.015 Gy from the OSLD measurements.

Conclusions

Cherenkov imaging combined with scintillation dosimetry presents an alternative methodology for CIED monitoring with the added benefit of instantly detecting deviations, enabling timely corrective actions or proper patient triage.

Background and Purpose

Treatment planning is a time-intensive task that could be automated. We aimed to develop a “single-click” workflow, fully deployed within a commercial treatment planning system (TPS), for autoplanning prostate radiotherapy treatment plans using predictions from a deep learning model (DLM).

Materials and Methods

Automatically generated treatment plans were created with a single script, executed from within a commercial TPS scripting environment, that performed two stages sequentially. Initially, a 3D dose distribution was predicted with a ResUNet DLM. The DLM was trained and validated using previously treated datasets (n = 120) which used 3D contours as inputs. Following this, dose predictions were converted into treatment plans by extracting dose-volume metrics from the predictions to use as objectives for the inverse optimizer within the TPS. An independent test dataset (n = 20) was used to evaluate the similarity between automated and clinical plans.

Results

For planning target volumes, the median percentage difference and interquartile range between the automatically generated plans and clinical plans were 0.4% [0.2-1.1%] for the V_100%, −0.5% [(−1.0)-(−0.2)%] for D_99% and −0.5% [(−1.0)-(−0.2)%] for D_95%. Bladder and rectum volume-at-dose objectives agreed within −6.1% [(−12.5)-0.9%]. The conversion of the DLM prediction into a treatment plan took 15 min [13-16 min].

Conclusions

An automatic plan generation workflow that uses a DL model with scripted optimization was fully deployed in a commercial TPS. Autoplans were compared to previously treated clinical plans and were found to be non-inferior.

Background and purpose

Image-based data mining (IBDM) requires spatial normalisation to reference anatomy, which is challenging in breast radiotherapy due to variations in the treatment position, breast shape and volume. We aim to optimise spatial normalisation for breast IBDM.

Materials and methods

Data from 996 patients treated with radiotherapy for early-stage breast cancer, recruited in the REQUITE study, were included. Patients were treated supine (n = 811), with either bilateral or ipsilateral arm(s) raised (551/260, respectively) or in prone position (n = 185). Four deformable image registration (DIR) configurations for extrathoracic spatial normalisation were tested. We selected the best-performing DIR configuration and further investigated two pathways: i) registering prone/supine cohorts independently and ii) registering all patients to a supine reference. The impact of arm positioning in the supine cohort was quantified. DIR accuracy was estimated using Normalised Cross Correlation (NCC), Dice Similarity Coefficient (DSC), mean Distance to Agreement (MDA), 95 % Hausdorff Distance (95 %HD), and inter-patient landmark registration uncertainty (ILRU).

Results

DIR using B-spline and normalised mutual information (NMI) performed the best across all evaluation metrics. Supine-supine registrations yielded highest accuracy (0.98 ± 0.01, 0.91 ± 0.04, 0.23 ± 0.19 cm, 1.17 ± 1.18 cm, 0.51 ± 0.26 cm for NCC, DSC, MDA, 95 %HD, and ILRU), followed by prone-prone and supine-prone registrations. Arm positioning had no significant impact on registration performance. For the best DIR strategy, uncertainty of 0.44 and 0.81 cm in the breast and shoulder regions was found.

Conclusions

B-spline algorithm using NMI and registered supine and prone cohorts independently provides the most optimal spatial normalisation strategy for breast IBDM.

Background and purpose

Monte Carlo (MC) based dose calculations are widely used in radiotherapy with a low statistical uncertainty, being accurate but slow. Increasing the uncertainty accelerates the calculation, but reduces quality. In online adaptive planning, however, dose is recalculated every treatment fraction, potentially decreasing the cumulative calculation error. This study aimed to evaluate the effect of higher MC statistical uncertainty in the context of daily online plan adaptation.

Materials and methods

For twenty prostate cancer patients, daily plans were simulated for 5 fractions and three modes of variation: rigid whole body translations, local-rigid prostate translations and local-rigid prostate rotations. For each mode and fraction, adaptive plans were generated from a clinical reference plan using three MC uncertainty values: 1 % (standard), 2 % and 3 % per plan. Dose-volume criteria were evaluated for accumulated doses, checking plan acceptability and comparing higher uncertainty plans to the standard.

Results

Increasing the statistical uncertainty setting from 1 % to 2–3 % caused an accumulated median target D_98% reduction of 0.1 Gy, with interquartile ranges (IQRs) up to 0.12 Gy. Rectum V_35Gy increased in median up to 0.16 cm³ with IQRs up to 0.33 cm³. The bladder V_28Gy and V_32Gy showed median increases up to 0.24 %-point, with IQRs up to 0.54 %-point. Using 2 % uncertainty reduced calculation times by more than a minute for all modes of variation, with no further time gain when increasing to 3 %.

Conclusion

A 2–3 % MC statistical uncertainty was clinically feasible. Using a 2 % uncertainty setting reduced calculation times at the cost of limited relative dose-volume differences.

Background and purpose

In vivo dosimetry is not standard in brachytherapy and some errors go undetected. The aim of this study was to evaluate the accuracy of multi-channel vaginal cylinder pulsed dose-rate brachytherapy using in vivo dosimetry.

Materials and methods

In vivo dosimetry data was collected during the years 2019–2022 for 22 patients (32 fractions) receiving multi-channel cylinder pulsed dose-rate brachytherapy. An inorganic scintillation detector was inserted in a cylinder channel. Each fraction was analysed as independent data sets. In vivo dosimetry-based source-tracking was used to determine the relative source-to-detector position. Measured dose was compared to planned and re-calculated source-tracking based doses. Assuming no change in organ and applicator geometry throughout treatment, the planned and source-tracking based dose distributions were compared in select volumes via γ-index analysis and dose-volume-histograms.

Results

The mean ± SD planned vs. measured dose deviations in the first pulse were 0.8 $\pm$ 5.9 %. In 31/32 fractions the deviation was within the combined in vivo dosimetry uncertainty (averaging 9.7 %, k = 2) and planning dose calculation uncertainty (1.6 %, k = 2). The dwell-position offsets were < 2 mm for 88 % of channels, with the largest being 5.1 mm (4.0 mm uncertainty, k = 2). 3 %/2 mm γ pass-rates averaged 97.0 % (clinical target volume (CTV)), 100.0 % (rectum), 99.9 % (bladder). The mean ± SD deviation was −1.$1$ ± 2.9 % for CTV D98, and −0.2 ± 0.9 % and −1.2 ± 2.5 %, for bladder and rectum D2cm³ respectively, indicating good agreement between intended and delivered dose.

Conclusions

In vivo dosimetry verified accurate and stable dose delivery in multi-channel vaginal cylinder based pulsed dose-rate brachytherapy.

This work investigates the use of a multi-2D cine magnetic resonance imaging-based comprehensive motion monitoring (CMM) system for the assessment of prostate intrafraction 3D drifts. The data of six healthy volunteers were analyzed and the values of a clinically-relevant registration quality factor metric exported by CMM were presented. Additionally, the CMM-derived prostate motion was compared to a 3D-based reference and the 2D-3D tracking agreement was reported. Due to the low quality of SI motion tracking (often $>$2 mm tracking mismatch between anatomical planes) we conclude that further improvements are desirable prior to clinical introduction of CMM for prostate drift corrections.

Background and purpose

The impact of intrafractional motion and deformations on clinical radiotherapy delivery has so far only been investigated by simulations as well as point and planar dose measurements. The aim of this study was to combine anthropomorphic 3D dosimetry with a deformable abdominal phantom to measure the influence of intra-fractional motion and gating in photon radiotherapy and evaluate the applicability in proton therapy.

Material and methods

An abdominal phantom was modified to hold a deformable anthropomorphic 3D dosimeter shaped as a human liver. A liver-specific photon radiotherapy and a proton pencil beam scanning therapy plan were delivered to the phantom without motion as well as with 12 mm sinusoidal motion while using either no respiratory gating or respiratory gating.

Results

Using the stationary irradiation as reference the local 3 %/2 mm 3D gamma index pass rate of the motion experiments in the planning target volume (PTV) was above 97 % (photon) and 78 % (proton) with gating whereas it was below 74 % (photon) and 45 % (proton) without gating.

Conclusions

For the first time a high-resolution deformable anthropomorphic 3D dosimeter embedded in a deformable abdominal phantom was applied for experimental validation of both photon and proton treatments of targets exhibiting respiratory motion. It was experimentally shown that gating improves dose coverage and the geometrical accuracy for both photon radiotherapy and proton therapy.